cooperative trajectory control for synchronizing the movement of two connected and autonomous vehicles separated in a mixed traffic flow
cooperative trajectory control for synchronizing the movement of two connected and autonomous vehicles separated in a mixed traffic flow
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协作轨迹控制，用于同步混合交通流中分离的两辆联网自动驾驶车辆的运动
DOI：
10.1016/j.trb.2023.05.006
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Du, Lili
中科院分区：
文献类型：
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作者：
Qiu, Jiahua;Du, Lili
当互联和自动驾驶车辆（CAV）在未来被广泛使用时，我们可以预见许多重要的应用，例如编队和自主警察巡逻，这需要两个CAV，最初在包括CAV和人类驾驶车辆（HDV）的混合交通流中分开，以快速接近彼此，然后保持稳定的车辆跟随模式。整个过程不应危及周边交通安全和效率。现有的文献对CAV的同步控制没有很好的研究，本文的研究试图部分弥补这一空白。为此，我们开发了一种嵌入混合整数非线性规划（MINLP-MPC）的模型预测控制模型，该模型集成了微观和宏观交通流模型，以捕获混合交通流动态。具体而言，MPC将在每个离散时间戳生成控制律，以管理两个主体CAV的微观运动，同时通过公认的车辆跟驰模型预测其相邻车辆的运动，并通过宏观交通模型（如细胞传输模型（CTM））估计远上游交通的响应。MINLP-MPC是多目标的，寻求维持同步和流量效率。为了生成这种平衡良好的最优控制，我们注意到同步经历了两个不同的阶段，依次完成追赶和排队任务。因此，我们转移MINLP-MPC的混合MPC系统由两个顺序MPC，分别优先追赶和排队控制。然后，我们开发了一个加权策略来调整控制优先级自适应。从数学上证明了模型预测控制的递归可行性。此外，我们推广的MPC和混合MPC系统，使多车辆同步。建立在NGSIM数据集上的数值研究证明了我们的方法在不同拥塞水平和CAV穿透下的效率和有效性。
When connected and autonomous vehicles (CAVs) are widely used in the future, we can foresee many essential applications, such as platoon formation and autonomous police patrolling, which need two CAVs, initially separated in a mixed traffic flow involving CAVs and human-drive vehicles (HDVs), to quickly approach each other and then keep a stable car-following mode. The entire process should not jeopardize surrounding traffic safety and efficiency. The existing literature has not studied this CAV synchronization control well, and this study seeks to make up this gap partially. To do that, we developed a Model Predictive Control model embedded with a mixed-integer nonlinear program (MINLP-MPC), which integrates micro- and macro-traffic flow models to capture hybrid traffic flow dynamics. Specifically, the MPC will generate control law at each discrete timestamp to manage the microscopic movements of the two subject CAVs while predicting their neighboring vehicles’ movement by well-accepted car-following models and estimating the distant upstream traffic’ response by the macroscopic traffic model such as cell transmission model (CTM). The MINLP-MPC is multi-objective, seeking to sustain both synchronization and traffic efficiencies. To generate such well-balanced optimal control, we noticed that the synchronization experiences two distinct phases, sequentially completing the catch-up and platooning tasks. Accordingly, we transferred MINLP-MPC to a hybrid MPC system consisting of two sequential MPCs, respectively prioritizing the catch-up and platooning control. Then, we developed a weighting strategy to tune the control priorities adaptively. The recursive feasibility of the MPC is mathematically proved. Furthermore, we generalized the MPC and the hybrid MPC system to enable multi-vehicle synchronization. A numerical study built upon the NGSIM dataset demonstrates the efficiency and effectiveness of our approaches under different congestion levels and CAV penetrations.